class_docstr: Regresses one or more floating point values given a single document.
  
  For a full list of configuration options, see `finetune.config`.
  
  :param config: A config object generated by `finetune.config.get_config` or None (for default config).
  :param \**kwargs: key-value pairs of config items to override.
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docstr: Produces a list of most likely output values as determined by the fine-tuned model.
  
  :param X: list or array of text to embed.
  :returns: list of floats
docstr_tok: ["Produces","a","list","of","most","likely","output","values","as","determined","by","the","fine-tuned","model",".",":","param","X",":","list","or","array","of","text","to","embed",".",":","returns",":","list","of","floats"]
code: def _predict(self, zipped_data, **kwargs):
      """
      Produces a list of most likely output values as determined by the fine-tuned model.
  
      :param X: list or array of text to embed.
      :returns: list of floats
      """
      all_labels = []
      for (
          _,
          _,
          start_of_doc,
          end_of_doc,
          label,
          _,
          _,
          _,
      ) in self.process_long_sequence(zipped_data, **kwargs):
          if start_of_doc:
              # if this is the first chunk in a document, start accumulating from scratch
              doc_labels = []
  
          doc_labels.append(label)
  
          if end_of_doc:
              # last chunk in a document
              means = np.mean(doc_labels, axis=0)
              label = (
                  self.input_pipeline.label_encoder.inverse_transform(
                      [means]
                  )
              )
              all_labels.append(list(label))
      return all_labels
  
file_path: finetune/finetune/target_models/regressor.py
imports: import numpy
  from finetune.base import BaseModel
  from finetune.encoding.target_encoders import RegressionEncoder
  from finetune.nn.target_blocks import regressor
  from finetune.input_pipeline import BasePipeline
label_desc: Use image node_id 4 for calling the Regressor obj's underlying member method code with example usage: obj._predict(zipped_data) and returns: all_labels